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公式動画&関連する動画 [Designing Memory Systems for AI Agents | MongoDB.local London 2026]
Watch more of .local London 2026 → https://www.youtube.com/playlist?list=PL4RCxklHWZ9tH01MTlChYwUqN8Cm2tl2r
Speakers:
Afi Gbadago, Senior Developer Advocate, MongoDB
You'll need your laptop to fully participate in this hands-on session. AI agents need memory to maintain context across sessions, learn from experience, and handle long-running tasks. The challenge? Deciding what to remember, where to store it, and how to retrieve it when it matters. In this workshop, you'll learn a practical framework for architecting memory systems that actually work in production.
We'll cover: · Types of memory in agentic systems · Storage patterns: Where to persist memories and how to structure them for retrieval · Retrieval strategies: Combining vector search with metadata, recency, and other signals · Memory lifecycle: When to create, update, or prune memories to keep your system performant You'll apply this framework by building memory into an AI agent and seeing how different design choices impact behavior.
00:00:00 - Introduction & Workshop Overview
00:00:40 - The GenAI Value Problem: Why AI Systems Fail
00:01:40 - Stateless vs. Stateful: Why AI Agents Need Memory
00:02:43 - Accessing the Jupyter Notebook Lab Space
00:03:32 - Framing AI Memory Around Human Cognition
00:04:09 - Deep Dive: Short-Term Memory & Session History
00:04:23 - Deep Dive: Semantic, Procedural, & Episodic Long-Term Memory
00:04:49 - Working Memory & The LLM Scratchpad Explained
00:05:33 - Lab Exercise 1: Setting Up MongoDB Collections & Indexes
00:05:58 - CRUD Principles Applied to AI Memory Systems
00:06:23 - Designing & Extracting Memory Traces
00:07:12 - Data Modeling for AI Memories in JSON/BSON
00:07:56 - Retrieval Strategies: Text Search vs. Vector Search
00:08:45 - Implementing Auto-Embeddings on MongoDB Atlas
00:08:59 - Maximizing Accuracy with Hybrid Search
00:09:11 - Leveraging Aggregation Pipelines for Multi-Stage Retrieval
00:09:35 - Updating Memory: Overwriting vs. Temporal Versioning
00:09:59 - Deleting Memory: TTL Indexes & Performance Pruning
00:10:44 - Coding a Gemini-Powered AI Assistant Function
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